基于延迟惩罚成本的生产物流瓶颈辨识及其预测研究

发布时间：2018-04-21 20:03

本文选题：生产物流 + 延迟惩罚成本　；参考：《浙江工业大学》2011年硕士论文

【摘要】：生产物流瓶颈是优化生产物流的关键,但是生产物流具有动态性和复杂性,使得瓶颈位置不断变化,因此生产物流瓶颈的辨识及其预测显得特别重要。针对生产物流中的半成品生产物流问题,开展了瓶颈辨识及其预测研究,主要内容包括： 1.基于延迟惩罚成本的生产物流瓶颈辨识研究。针对半成品生产物流中生产延迟问题,提出了基于延迟惩罚成本的瓶颈辨识研究。分析生产物流瓶颈与生产瓶颈的区别,阐述了生产物流瓶颈问题；以生产延迟产生的惩罚成本最大为生产物流瓶颈的定义；延迟惩罚成本函数具体为延迟时间和延迟次数的加权和,以此为目标函数建立瓶颈模型；设计了由订单层、部件层、工序层组成的三层遍历算法,以求解模型中目标函数的生产延迟时间和延迟次数两变量；通过实例,说明了瓶颈辨识方法的有效性及合理性。 2.基于瓶颈指数的生产物流瓶颈预测研究。基于延迟惩罚成本的瓶颈辨识研究为事后辨识,针对此不足,提出了基于瓶颈指数的生产物流瓶颈预测研究。其中包括三部分内容： (1)瓶颈因素分析。分析生产延迟的形成过程,确定了瓶颈的本质属性为有效产能需求比；从影响有效产能的设备综合利用率和在制品库存两方面,定量分析了瓶颈主成因素；由此展开,定性分析瓶颈细分因素,全面剖析了影响生产物流瓶颈的因素。 (2)瓶颈预测模型构建。在量化有效产能的基础上,提出瓶颈指数概念,以此建立了瓶颈的预测模型；提出了瓶颈转移指数和敏感指数概念,以分析瓶颈的转移趋势及其对因素的敏感程度。 (3)瓶颈因素预测模型构建。详细介绍神经网络的理论,构建了瓶颈因素预测的神经网络模型；在实例中应用MATLAB进行仿真,验证了方法的有效性。
[Abstract]:The bottleneck of production logistics is the key to optimize production logistics, but the dynamic and complexity of production logistics make the bottleneck position change constantly, so it is very important to identify and predict the bottleneck of production logistics. Aiming at the problem of semi-finished product logistics in production logistics, the bottleneck identification and its prediction are studied. The main contents are as follows: 1. Research on bottleneck Identification of production Logistics based on delay penalty cost. Aiming at the problem of production delay in semi-finished product production logistics, a bottleneck identification study based on delay penalty cost is proposed. This paper analyzes the difference between production logistics bottleneck and production bottleneck, expounds the bottleneck problem of production logistics, takes the maximum penalty cost of production delay as the definition of production logistics bottleneck, and analyzes the difference between production logistics bottleneck and production logistics bottleneck. The delay penalty cost function is the weighted sum of delay time and delay times, which is the objective function to establish bottleneck model, and a three-layer traversal algorithm is designed, which is composed of order layer, component layer and process layer. The production delay time and delay times of the objective function in the model are solved, and the effectiveness and rationality of the bottleneck identification method are illustrated by an example. 2. Research on bottleneck Prediction of production Logistics based on bottleneck Index. The research of bottleneck identification based on delay penalty cost is hindsight identification. In view of this deficiency, a bottleneck prediction research based on bottleneck index is proposed. There are three parts: Analysis of bottleneck factors. This paper analyzes the forming process of production delay, determines that the essential attribute of bottleneck is the ratio of effective capacity demand, and quantitatively analyzes the main factors of bottleneck from two aspects: the comprehensive utilization ratio of equipment and the inventory of in-process products, which affect the effective production capacity. Qualitative analysis of bottleneck subdivision factors, comprehensive analysis of factors affecting the bottleneck of production logistics. 2) Construction of bottleneck prediction model. On the basis of quantifying effective productivity, the concept of bottleneck index is put forward to establish the prediction model of bottleneck, and the concepts of bottleneck transfer index and sensitive index are put forward to analyze the trend of bottleneck transfer and its sensitivity to factors. The prediction model of bottleneck factors is constructed. The theory of neural network is introduced in detail, and the neural network model of bottleneck factor prediction is constructed, and the effectiveness of the method is verified by MATLAB simulation in an example.
【学位授予单位】：浙江工业大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH186

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